Towards global insect biomonitoring with frugal methods.

None of the global targets for protecting nature are currently met, although humanity is critically dependent on biodiversity. A significant issue is the lack of data for most biodiverse regions of the planet where the use of frugal methods for biomonitoring would be particularly important because the available funding for monitoring is insufficient, especially in low-income countries. We here discuss how three approaches to insect biomonitoring (computer vision, lidar, DNA sequences) could be made more frugal and urge that all biomonitoring techniques should be evaluated for global suitability before becoming the default in high-income countries. This requires that techniques popular in high-income countries should undergo a phase of 'innovation through simplification' before they are implemented more broadly. We predict that techniques that acquire raw data at low cost and are suitable for analysis with AI (e.g. images, lidar-signals) will be particularly suitable for global biomonitoring, while techniques that rely heavily on patented technologies may be less promising (e.g. DNA sequences). We conclude the opinion piece by pointing out that the widespread use of AI for data analysis will require a global strategy for providing the necessary computational resources and training. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Towards holistic insect monitoring: species discovery, description, identification and traits for all insects.

Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Evaluating Toxicity of Chemicals using a Zebrafish Vibration Startle Response Screening System.

We developed a simple screening system for the evaluation of neuromuscular and general toxicity in zebrafish embryos. The modular system consists of electrodynamic transducers above which tissue culture dishes with embryos can be placed. Multiple such loudspeaker-tissue culture dish pairs can be combined. Vibrational stimuli generated by the electrodynamic transducers induce a characteristic startle and escape response in the embryos. A belt-driven linear drive sequentially positions a camera above each loudspeaker to record the movement of the embryos. In this way, alterations to the startle response due to lethality or neuromuscular toxicity of chemical compounds can be visualized and quantified. We present an example of the workflow for chemical compound screening using this system, including the preparation of embryos and treatment solutions, operation of the recording system, and data analysis to calculate benchmark concentration values of compounds active in the assay. The modular assembly based on commercially available simple components makes this system both economical and flexibly adaptable to the needs of particular laboratory setups and screening purposes.

Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels.

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish's position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition.

DiversityScanner: Robotic handling of small invertebrates with machine learning methods.

Invertebrate biodiversity remains poorly understood although it comprises much of the terrestrial animal biomass, most species and supplies many ecosystem services. The main obstacle is specimen-rich samples obtained with quantitative sampling techniques (e.g., Malaise trapping). Traditional sorting requires manual handling, while molecular techniques based on metabarcoding lose the association between individual specimens and sequences and thus struggle with obtaining precise abundance information. Here we present a sorting robot that prepares specimens from bulk samples for barcoding. It detects, images and measures individual specimens from a sample and then moves them into the wells of a 96-well microplate. We show that the images can be used to train convolutional neural networks (CNNs) that are capable of assigning the specimens to 14 insect taxa (usually families) that are particularly common in Malaise trap samples. The average assignment precision for all taxa is 91.4% (75%-100%). This ability of the robot to identify common taxa then allows for taxon-specific subsampling, because the robot can be instructed to only pick a prespecified number of specimens for abundant taxa. To obtain biomass information, the images are also used to measure specimen length and estimate body volume. We outline how the DiversityScanner can be a key component for tackling and monitoring invertebrate diversity by combining molecular and morphological tools: the images generated by the robot become training images for machine learning once they are labelled with taxonomic information from DNA barcodes. We suggest that a combination of automation, machine learning and DNA barcoding has the potential to tackle invertebrate diversity at an unprecedented scale.

Long photoperiod impairs learning in male but not female medaka.

Day length in conjunction with seasonal cycles affects many aspects of animal biology. We have studied photoperiod-dependent alterations of complex behavior in the teleost, medaka (Oryzias latipes), a photoperiodic breeder, in a learning paradigm whereby fish have to activate a sensor to obtain a food reward. Medaka were tested under a long (14:10 LD) and short (10:14 LD) photoperiod in three different groups: mixed-sex, all-males, and all-females. Under long photoperiod, medaka mixed-sex groups learned rapidly with a stable response. Unexpectedly, males-only groups showed a strong learning deficit, whereas females-only groups performed efficiently. In mixed-sex groups, female individuals drove group learning, whereas males apparently prioritized mating over feeding behavior resulting in strongly reduced learning performance. Under short photoperiod, where medaka do not mate, male performance improved to a level similar to that of females. Thus, photoperiod has sex-specific effects on the learning performance of a seasonal vertebrate.

Recent Developments in Ozone Sensor Technology for Medical Applications.

There is increasing interest in the utilisation of medical gases, such as ozone, for the treatment of herniated disks, peripheral artery diseases, and chronic wounds, and for dentistry. Currently, the in situ measurement of the dissolved ozone concentration during the medical procedures in human bodily liquids and tissues is not possible. Further research is necessary to enable the integration of ozone sensors in medical and bioanalytical devices. In the present review, we report selected recent developments in ozone sensor technology (2016-2020). The sensors are subdivided into ozone gas sensors and dissolved ozone sensors. The focus thereby lies upon amperometric and impedimetric as well as optical measurement methods. The progress made in various areas-such as measurement temperature, measurement range, response time, and recovery time-is presented. As inkjet-printing is a new promising technology for embedding sensors in medical and bioanalytical devices, the present review includes a brief overview of the current approaches of inkjet-printed ozone sensors.

Automated high-throughput heartbeat quantification in medaka and zebrafish embryos under physiological conditions.

Accurate quantification of heartbeats in fish models is an important readout to study cardiovascular biology, disease states and pharmacology. However, dependence on anaesthesia, laborious sample orientation or requirement for fluorescent reporters have hampered the use of high-throughput heartbeat analysis. To overcome these limitations, we established an efficient screening assay employing automated label-free heart rate determination of randomly oriented, non-anesthetized medaka (Oryzias latipes) and zebrafish (Danio rerio) embryos in microtiter plates. Automatically acquired bright-field data feeds into an easy-to-use HeartBeat software with graphical user interface for automated quantification of heart rate and rhythm. Sensitivity of the assay was demonstrated by profiling heart rates during entire embryonic development. Our analysis revealed rapid adaption of heart rates to temperature changes, which has implications for standardization of experimental layout. The assay allows scoring of multiple embryos per well enabling a throughput of >500 embryos per 96-well plate. In a proof of principle screen for compound testing, we captured concentration-dependent effects of nifedipine and terfenadine over time. Our novel assay permits large-scale applications ranging from phenotypic screening, interrogation of gene functions to cardiovascular drug development.

Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Medaka (Oryzias latipes) and zebrafish (Danio rerio) contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have developed a framework that segments the ventricle of a medaka hatchling from image sequences and subsequently quantifies ventricular dimensions.

DIY Automated Feeding and Motion Recording System for the Analysis of Fish Behavior.

Fish species such as medaka or zebrafish are widely used as animal models to study physiology, disease development, and treatment efficacy. They are also used to study the rapidly growing field of behavior research, such as social interactions, anxiety, and the influence of environmental factors. Here we describe an automated experimental setup allowing the recording of general locomotor activity in combination with a food-on-demand system. It can simply be built with some basic electronic knowledge. Our setup enables the recording of locomotor and feeding activity of several fish for long-term studies, excluding disturbing external influences. A description of the automated recording system is given, as well as examples of recordings to illustrate its applicability for the study of fish behavior. The construction manual and operation instructions can be downloaded for free.

Therapeutic Chemical Screen Identifies Phosphatase Inhibitors to Reconstitute PKB Phosphorylation and Cardiac Contractility in ILK-Deficient Zebrafish.

Patients with inherited dilated cardiomyopathy (DCM) often suffer from severe heart failure based on impaired cardiac contractility leading to increased morbidity and mortality. Integrin-linked kinase (ILK) as a part of the cardiac mechanical stretch sensor was found to be an essential genetic regulator of cardiac contractility. Integrin-linked kinase localizes to z-disks and costameres in vertebrate hearts and regulates the activity of the signaling molecule protein kinase B (PKB/Akt) by controlling its phosphorylation. Despite identification of several potential drug targets in the ILK signaling pathway, pharmacological treatment strategies to restore contractile function in ILK-dependent cardiomyopathies have not been established yet. In recent years, the zebrafish has emerged as a valuable experimental system to model human cardiomyopathies as well as a powerful tool for the straightforward high-throughput in vivo small compound screening of therapeutically active substances. Using the ILK deficient zebrafish heart failure mutant main squeeze (msq), which shows reduced PKB phosphorylation and thereby impaired cardiac contractile force, we identified here, in an automated small compound screen, the protein phosphatase inhibitors calyculin A and okadaic acid significantly restoring myocardial contractile function by reconstituting PKB phosphorylation in msq ILK-deficient zebrafish embryos.

Automated Versatile DIY Microscope Platform.

A versatile robot platform is presented that can be used to design low-cost custom made microscopes in do-ityourself construction. All components like the framework, the linear drives, robot controller and driver, the illumination and the camera are described as well as optional features like fluorescence microscopy and auto-focus. Finally, an application for automated imaging of 3D-cell cultures in 96-well microplates is presented.

Zebrafish: A Pharmacogenetic Model for Anesthesia.

General anesthetics are small molecules that interact with and effect the function of many different proteins to promote loss of consciousness, amnesia, and sometimes, analgesia. Owing to the complexity of this state transition and the transient nature of these drug/protein interactions, anesthetics can be difficult to study. The zebrafish is an emerging model for the discovery of both new genes required for the response to and side effects of anesthesia. Here we discuss the tools available to manipulate the zebrafish genome, including both genetic screens and genome engineering approaches. Additionally, there are various robust behavior assays available to study anesthetic and other drug responses. These assays are available for single-gene study or high throughput for genetic or drug discovery. Finally, we present a case study of using propofol as an anesthetic in the zebrafish. These techniques and protocols make the zebrafish a powerful model to study anesthetic mechanisms and drug discovery.

Fully Automated Pipetting Sorting System for Different Morphological Phenotypes of Zebrafish Embryos.

Systems biology methods, such as transcriptomics and metabolomics, require large numbers of small model organisms, such as zebrafish embryos. Manual separation of mutant embryos from wild-type embryos is a tedious and time-consuming task that is prone to errors, especially if there are variable phenotypes of a mutant. Here we describe a zebrafish embryo sorting system with two cameras and image processing based on template-matching algorithms. In order to evaluate the system, zebrafish rx3 mutants that lack eyes due to a patterning defect in brain development were separated from their wild-type siblings. These mutants show glucocorticoid deficiency due to pituitary defects and serve as a model for human secondary adrenal insufficiencies. We show that the variable phenotypes of the mutant embryos can be safely distinguished from phenotypic wild-type zebrafish embryos and sorted from one petri dish into another petri dish or into a 96-well microtiter plate. On average, classification of a zebrafish embryo takes approximately 1 s, with a sensitivity and specificity of 87% to 95%, respectively. Other morphological phenotypes may be classified and sorted using similar techniques.

Design of a mechanism for converting the energy of knee motions by using electroactive polymers.

Harvesting energy from human body motions has become a promising option to prolong battery life for powering medical devices for autonomy. Up to now, different generating principles including dielectric electroactive polymers (DEAPs) have been suggested for energy conversion. However, there is a lack of mechanisms that are specifically designed to convert energy with DEAPs. In a proof of concept study, a mechanical system was designed for stretching DEAPs in those phases of the gait cycle, in which the muscles mainly perform negative work. Rotational movements of the knee joint are transformed into linear movements by using a cable pull. The DEAP can be charged during the stretching phase and discharged during releasing and allows for the conversion of kinetic energy into electric energy. To evaluate the concept, tests were conducted. It was found that the developed body energy harvesting (BEH) system has a performance in the range of 24-40 µW at normal walking speed. The converted energy is sufficient for powering sensors in medical devices such as active orthoses or prostheses.

Automated phenotype pattern recognition of zebrafish for high-throughput screening.

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79-99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.

An automated and high-throughput Photomotor Response platform for chemical screens.

The zebrafish (Danio rerio) is a well-established vertebrate model organism. Its embryos are used extensively in biology and medicine to perform chemical screens to identify drug candidates or to evaluate teratogenicity and embryotoxicity of substances. Behavioral readouts are increasingly used to assess the effects of compounds on the nervous system. Early stage zebrafish show characteristic behavioral features at stages between 30 and 42 hours post fertilization (hpf) when exposed to a short and bright light flash. This so-called Photomotor Response (PMR) is a reaction of the nervous system of the fish and can be used as a marker in screenings for neuroactive chemicals. To probe a broad and diverse chemical space, many different substances have to be tested and repeated observations are necessary to warrant statistical significance of the results. Although PMR-based chemical screens must use a large number of specimens, there is no sophisticated, automated high-throughput platform available which ensures minimal human intervention. Here we report a PMR platform that was developed by combining an improved automatic sample handling with a remotely controllable microscope setup and an image analysis pipeline. Using infrared illumination during automatic sample preparation, we were able to eliminate excess amounts of visible light that could potentially alter the response results. A remotely controlled microscope setup allows us to screen entire 96-well microtiter plates without human presence that could disturb the embryos. The development of custom video analysis software, including single egg detection, enables us to detect variance among treated specimens and extract easy to interpret numerical values representing the PMR motion. By testing several neuroactive compounds we validated the workflow that can be used to analyze more than one thousand zebrafish eggs on a single 96-well plate.

Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

Kinetic energy scavenging in a prosthetic foot using a fluidic system.

The use of active prostheses for the lower extremity is limited by the amount of electric energy stored in batteries. A promising way to extend their usage time is to convert motions generated by the human body during walking to electrical energy. A first functioning prototype was designed to transfer kinetic energy from heel contact and forefoot contact to a generator by using a fluidic system. Experimental results show that walking with the system generates an average electrical power of 0.8 W. The design of the energy scavenging system (ESS) is presented and results are discussed.